Deep Learning

Understand deep learning with this comprehensive guide! Dive into essential topics like Linear Algebra, Probability, and Numerical Computation, crucial for mastering machine learning algorithms. Understand Scalars, Vectors, Matrices, Tensors, Eigen decomposition, and more. Explore Probability Distributions, Conditional Probability, and Bayes' Rule to build a strong foundation in data science. This document is your ultimate resource, packed with vital knowledge for beginners and professionals ...

Deep learning is a specialized branch of machine learning that focuses on algorithms inspired by the structure and function of the human brain, known as artificial neural networks. It involves networks with multiple layers (hence the term "deep") that can learn to represent data with increasing levels of abstraction. Each layer in a deep learning model transforms the input data into more complex and meaningful representations, allowing the model to automatically discover features relevant ...

example problem on preceptron

Deep Learning – History - Key facts – Biological Neuron- Artificial Neuron – Mc Culloch Pitts Neuron, Limitations of MP Neuron – Perceptron – Multi-Layer Perceptron – Basics of Neural Networks :Neurons, Output Functions – Activation Functions – Weights and Biases

Deep Learning is a powerful subset of machine learning where artificial neural networks, inspired by the human brain, learn to perform tasks by processing vast amounts of data. It unravels complexity through layers of interconnected nodes, allowing it to recognize patterns, make predictions, and solve intricate problems, all while striving to mimic the adaptability and learning capacity of the human mind.

Deep Sequence Modeling 93 slides

Class notes; Introduction to Deep Learning 101 slides

Deep Learning Zero To Hero